The control apparatus of the present invention is a new type of Model Predictive Control (MPC) controller. MPC technologies, in general, use a process model to predict the future state of a process variable to be controlled, and then manipulate one or more process inputs (controller outputs) to minimize expected error between the prediction and the set points. MPC variations include model predictive heuristic control (MPHC) (Richalet et al., 1978), model algorithmic control (MAC) (Mahra et al, 1979), dynamic matrix control (DMC) (Cutler and Ramaker, 1980; Prett and Gillete, 1979), and linear dynamic matrix control (LDMC) (Garcia and Morshedi, 1986). In all of these methods, the model is an intrinsic part of the control algorithm and is used both for prediction and for computing the controller response.
The basic idea behind MPC is to model a process with the step or impulse response vector of the process. These models predict one or more process outputs from process inputs, and the optimum controller response is computed by means of linear optimization of a series of controller inputs. In existing methods, the optimization problem is solved in real-time, at each step of the controller. Quadratic programming (QP) and linear quadratic regulator (LQ) methods are used for this minimization.
Several problems in prior MPC techniques have limited their use. Most formulations of MPC require the engineer to specify many adjustable parameters to describe the desired closed loop response. An expert on both the process and the controller must make these adjustments, because making the desired response too sharp can lead to unstable performance in some cases, even when the model is a good fit to the process. Furthermore, MPC techniques are not inherently better than classic control, because in theory, there is an equivalent classic controller for every MPC controller.
All MPC controllers that perform QP or LQ optimization on every update interval incur substantial computational overhead. Applications of MPC control are limited to those where a single computer is dedicated to controlling a small number of process variables, typically at intervals of at least several seconds. Most current applications of MPC are performed as supervisory control, where the MPC controller manipulates setpoints of conventional controllers to achieve some overall optimum process performance.
Also, some MPC controllers, particularly those not using QP methods, can become unstable in the presence of constraints found on all real-world processes. The QP-MPC controllers have less difficulty dealing with constraints, but are more computationally complex. In addition, most MPC controllers require either prior knowledge of the process dynamics, or off-line tuning procedures.
In all cases, mismatch between the process and the process model can produce unstable performance. This is often the case when the process response is non-linear. With the high degree of complexity of existing MPC controllers, it is often a difficult task for the engineer using these systems to establish optimum tuning parameters that will provide high control performance over the entire desired operating range.
To solve these problems, fuzzy logic and neural network approaches to continuous process control have been developed recently. Fuzzy logic makes it easier to incorporate heuristic rules into the behavior of a particular controller. This can make it easier to adapt continuous process controllers to specific applications. Neural networks use a generic non-linear modeling method to describe process behavior. The generic modeling of neural networks can be used to automatically develop a process dynamic model to be incorporated into an MPC controller in order to improve performance. However, a neural network model is a black box that yields little information from which to predict optimum control response.